Limits...
Interrogating the topological robustness of gene regulatory circuits by randomization

View Article: PubMed Central - PubMed

ABSTRACT

One of the most important roles of cells is performing their cellular tasks properly for survival. Cells usually achieve robust functionality, for example, cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior has been typically studied on the basis of experimentally verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment. Here we report a new computational method, named random circuit perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By applying RACIPE to simple toggle-switch-like motifs, we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal Transition (EMT), from which we identified four experimentally observed gene states, including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression.

No MeSH data available.


Related in: MedlinePlus

Application of RACIPE to coupled toggle-switch circuits.RACIPE was tested on coupled toggle-switch circuits, as illustrated at the top of the figure. (A) 2D probability density map of the RACIPE-predicted gene expression data projected to the 1st and 2nd principal component axes. (B) Average linkage hierarchical clustering analysis of the gene expression data from all the RACIPE models using the Euclidean distance. Each column corresponds to a gene, while each row corresponds to a stable steady state. The clustering analysis allows the identification of several robust gene states, whose characteristics were illustrated as circuit cartoons to the right of the heatmaps. The expression levels of each gene in these gene states are illustrated as low (grey), intermediate (blue), or high (red). See S6 and S7 Figs for the definitions.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5391964&req=5

pcbi.1005456.g005: Application of RACIPE to coupled toggle-switch circuits.RACIPE was tested on coupled toggle-switch circuits, as illustrated at the top of the figure. (A) 2D probability density map of the RACIPE-predicted gene expression data projected to the 1st and 2nd principal component axes. (B) Average linkage hierarchical clustering analysis of the gene expression data from all the RACIPE models using the Euclidean distance. Each column corresponds to a gene, while each row corresponds to a stable steady state. The clustering analysis allows the identification of several robust gene states, whose characteristics were illustrated as circuit cartoons to the right of the heatmaps. The expression levels of each gene in these gene states are illustrated as low (grey), intermediate (blue), or high (red). See S6 and S7 Figs for the definitions.

Mentions: To evaluate the effectiveness of RACIPE on larger circuits, we further applied the method to circuits with two to five coupled toggle-switch (CTS) motifs (Fig 5). Different from the above simple circuit motifs, the gene expression data obtained by RACIPE for these CTS motifs are now high-dimensional; thus in the scatter plot analysis we projected these data onto the first two principal components by principal component analysis (PCA). For each circuit, we observed distinct gene states from PCA for the RACIPE models (Fig 5A). More interestingly, the number of gene states found via PCA increases by one each time one more toggle switch is added to the circuit. Moreover, we applied HCA to the gene expression data, from which we identified the same gene states as from PCA (Fig 5B). At this stage, we can also assign high (red circles), intermediate (blue circles) or low expression (black circles) to each gene for every gene state. Unlike in Boolean network models, the assignment in RACIPE is based on the distribution of expression pattern from all the models in the ensemble (S6 and S7 Figs).


Interrogating the topological robustness of gene regulatory circuits by randomization
Application of RACIPE to coupled toggle-switch circuits.RACIPE was tested on coupled toggle-switch circuits, as illustrated at the top of the figure. (A) 2D probability density map of the RACIPE-predicted gene expression data projected to the 1st and 2nd principal component axes. (B) Average linkage hierarchical clustering analysis of the gene expression data from all the RACIPE models using the Euclidean distance. Each column corresponds to a gene, while each row corresponds to a stable steady state. The clustering analysis allows the identification of several robust gene states, whose characteristics were illustrated as circuit cartoons to the right of the heatmaps. The expression levels of each gene in these gene states are illustrated as low (grey), intermediate (blue), or high (red). See S6 and S7 Figs for the definitions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5391964&req=5

pcbi.1005456.g005: Application of RACIPE to coupled toggle-switch circuits.RACIPE was tested on coupled toggle-switch circuits, as illustrated at the top of the figure. (A) 2D probability density map of the RACIPE-predicted gene expression data projected to the 1st and 2nd principal component axes. (B) Average linkage hierarchical clustering analysis of the gene expression data from all the RACIPE models using the Euclidean distance. Each column corresponds to a gene, while each row corresponds to a stable steady state. The clustering analysis allows the identification of several robust gene states, whose characteristics were illustrated as circuit cartoons to the right of the heatmaps. The expression levels of each gene in these gene states are illustrated as low (grey), intermediate (blue), or high (red). See S6 and S7 Figs for the definitions.
Mentions: To evaluate the effectiveness of RACIPE on larger circuits, we further applied the method to circuits with two to five coupled toggle-switch (CTS) motifs (Fig 5). Different from the above simple circuit motifs, the gene expression data obtained by RACIPE for these CTS motifs are now high-dimensional; thus in the scatter plot analysis we projected these data onto the first two principal components by principal component analysis (PCA). For each circuit, we observed distinct gene states from PCA for the RACIPE models (Fig 5A). More interestingly, the number of gene states found via PCA increases by one each time one more toggle switch is added to the circuit. Moreover, we applied HCA to the gene expression data, from which we identified the same gene states as from PCA (Fig 5B). At this stage, we can also assign high (red circles), intermediate (blue circles) or low expression (black circles) to each gene for every gene state. Unlike in Boolean network models, the assignment in RACIPE is based on the distribution of expression pattern from all the models in the ensemble (S6 and S7 Figs).

View Article: PubMed Central - PubMed

ABSTRACT

One of the most important roles of cells is performing their cellular tasks properly for survival. Cells usually achieve robust functionality, for example, cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior has been typically studied on the basis of experimentally verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment. Here we report a new computational method, named random circuit perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By applying RACIPE to simple toggle-switch-like motifs, we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal Transition (EMT), from which we identified four experimentally observed gene states, including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression.

No MeSH data available.


Related in: MedlinePlus